Diagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representation
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Large amounts of rich, heterogeneous information nowadays routinely collected by health care providers across the world possess remarkable potential for the extraction of novel medical data and the assessment of different practices in real-world conditions. Specifically in this work our goal is to use Electronic Health Records (EHRs) to predict progression patterns of future diagnoses of ailments for a particular patient, given the patient’s present diagnostic history. Following the highly promising results of a recently proposed approach which introduced the diagnosis history vector representation of a patient’s diagnostic record, we introduce a series of improvements to the model and conduct thorough experiments that demonstrate its scalability, accuracy, and practicability in the clinical context. We show that the model is able to capture well the interaction between a large number of ailments which correspond to the most frequent diagnoses, show how the original learning framework can be adapted to increase its prediction specificity, and describe a principled, probabilistic method for incorporating explicit, human clinical knowledge to overcome semantic limitations of the raw EHR data.
Vasiljeva , I & Arandelovic , O 2017 , ' Diagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representation ' Journal of Computational Biology , vol. 24 , no. 8 , pp. 767-768 . https://doi.org/10.1089/cmb.2017.0023
Journal of Computational Biology
© 2017, Mary Ann Liebert. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at online.liebertpub.com / https://doi.org/10.1089/cmb.2017.0023
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